Methods and apparatus to detect people

ABSTRACT

Methods and apparatus to detect people are disclosed. An example apparatus includes a receiver to obtain respective light information for a plurality of segments of an image, each of the segments corresponding to a portion of a media exposure environment; a pulse identifier to detect a first human pulse pattern in the light information associated with a first one of the segments; and a grouper to determine whether the light information associated with a threshold amount of the segments proximate the first one of the segments includes the first human pulse pattern identified in the first one of the segments, and to generate a presence indication when the light information associated with the threshold amount of the segments proximate the first segment includes the first human pulse pattern.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moreparticularly, to methods and apparatus to detect people.

BACKGROUND

Audience measurement of media (e.g., content and/or advertisements, suchas broadcast television and/or radio programs and/or advertisements,streaming media, stored audio and/or video programs and/oradvertisements played back from a memory such as a digital videorecorder or a digital video disc, audio and/or video programs and/oradvertisements played via the Internet, video games, etc.) ofteninvolves collection of media identifying data (e.g., signature(s),fingerprint(s), code(s), channel information, time of presentationinformation, etc.) and people data (e.g., user identifiers, demographicdata associated with audience members, etc.). The media identifying dataand the people data can be combined to generate, for example, mediaexposure data indicative of amount(s) and/or type(s) of people that wereexposed to specific piece(s) of media.

In some audience measurement systems, the collected people data includesan amount of people in a media exposure environment (e.g., a televisionroom, a family room, a living room, a cafeteria at a place of businessor lounge, a television viewing section of a store, restaurant, a bar,etc.). The calculated amount of people in the environment can becorrelated with media being presented in the environment at theparticular date and time to provide exposure data (e.g., ratings data)for that media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example media exposure environmentincluding an example meter constructed in accordance with teachings ofthis disclosure.

FIG. 2 is a block diagram of an example implementation of the examplemeter of FIG. 1.

FIG. 3 is a block diagram of an example implementation of the exampleperson detector of FIG. 2.

FIG. 4 is an illustration of an example segmentation of the examplemedia exposure environment of FIG. 1 implemented by the example meter ofFIGS. 1 and/or 2.

FIG. 5 is an illustration of an example thermal image captured by theexample meter of FIGS. 1 and/or 2.

FIG. 6 is a flowchart representative of example machine readableinstructions that may be executed to implement the example persondetector of FIGS. 2 and/or 3.

FIG. 7 is a flowchart representative of example machine readableinstructions that may be executed to implement the example persondetector of FIGS. 2 and/or 3.

FIG. 8 is a block diagram of an example processing system capable ofexecuting the example machine readable instructions of FIGS. 5 and/or 6to implement the example person detector of FIGS. 2 and/or 3.

DETAILED DESCRIPTION

In some audience measurement systems, people data is collected for amedia exposure environment (e.g., a television room, a family room, aliving room, a bar, a restaurant, an office space, a cafeteria, etc.) bycapturing a series of images of the environment and analyzing the imagesto determine, for example, an identity of one or more persons present inthe media exposure environment, an amount of people present in the mediaexposure environment during one or more times and/or periods of time, anamount of attention being paid to a media presentation by one or morepersons, a gesture made by a person in the media exposure environment,etc. The people data can be correlated with, for example, mediaidentifying information corresponding to detected media to provideexposure data for that media. For example, an audience measuremententity (e.g., The Nielsen Company (US), LLC) can calculate ratings for afirst piece of media (e.g., a television program) by correlating mediaexposure data collected from a plurality of panelist sites with thedemographics of the panelists. For example, in each panelist sitewherein the first piece of media is detected in the monitoredenvironment at a first time, media identifying information for the firstpiece of media is correlated with presence information detected in theenvironment at the first time. The results from multiple panelist sitesare combined and/or analyzed to provide ratings representative of anaudience (e.g., an entire population, a demographic segment, etc.)exposed to the media.

To generate the people data, some systems attempt to recognize objectsas humans in image data representative of the monitored environment. Insuch systems, a tally is maintained for each frame of image data toreflect an amount of people in the environment at a particular time.However, image data processing is computationally expensive and hasdrawbacks, such as false positives. For example, a non-human object,such as a picture of a human face hanging on a wall, is sometimesmistaken for a human face, thereby improperly inflating theperson/audience tally for the corresponding frame. These and otherlimitations and/or inaccuracies can lead to an inaccurate tally ofpeople for individual frames. An inaccurate tally of people in a framecan negatively affect the accuracy of media exposure data generatedusing the tally. For example, a meter counting the people in a room mayalso be collecting media identifying information to identify media beingpresented (e.g., aurally and/or visually) in the room. With theidentification of the media and the amount of people in the room at agiven date and time, the meter can indicate how many people were exposedto the specific media and/or associate the demographics of the peoplewith the media to determine audience characteristics for the specificmedia. When person(s) are not detected or recognized as people, theexposure data for the identified media may be undercut (e.g., the mediais accredited with less viewers/listeners than had actually been exposedto the media). Alternatively, when false positives are detected, theexposure data for the identified media may be overstated (e.g., themedia is accredited with more viewers/listeners than had actually beenexposed to the media). Additionally, systems that capture image data mayraise privacy concerns for some.

Example methods, apparatus, and articles of manufacture disclosed hereindetect people in an environment in an efficient, accurate and privatemanner that avoids drawbacks of known image data recognition systems,such as computationally expensive image processing person recognitionalgorithms and costly types of image capture equipment. Examplesdisclosed herein collect light information for a plurality of definedsegments of an environment (e.g., a living room including a mediapresentation device such as a television and/or audio). Examplesdisclosed herein recognize that people are typically horizontallyarranged (e.g., sitting across furniture) in the environment, ratherthan vertically. Accordingly, examples disclosed herein divide theenvironment into a plurality of vertical segments or strips and collectthe light information for each of the segments. In some examples, thelight information includes an aggregate brightness value for thecorresponding segment for a first time.

Examples disclosed herein collect sequences of the light informationover periods of time. Thus, in some examples, light information gatheredfor each segment includes a sequence of brightness values. In someexamples disclosed herein, the light information is collected via anarray of light sensors arranged horizontally behind a light guide (e.g.,a lens). In some examples disclosed herein, each of the light sensorscorresponds to one of the defined segments of the environment. In someexamples disclosed herein, the light guide directs light from each ofthe segments of the environment to the respective ones of the lightsensors. While examples disclosed herein are described below ascollecting the light information via an array of light sensors,additional or alternative collection techniques and/or equipment can beused. For example, two-dimensional image data representative of theenvironment can be captured and brightness information for each of aplurality of segments can be obtained from the image data.

Examples disclosed herein analyze the light information for each segmentover a period of time (e.g., a brightness value sequence) to determinewhether the light information is indicative of a person being present inthe segment. Examples disclosed herein determine whether the lightinformation of a particular segment over the period of time includes apattern indicative of a human pulse. That is, examples disclosed hereindetermine whether a brightness value sequence includes apulse-indicative pattern. A face of a person varies in brightness due topulse-driven blood flow. The pulse of a person, especially while at rest(e.g., watching television) has a pattern. Thus, the variance inbrightness of a face includes a pattern that corresponds to thepulse-driven blood flow. Examples disclosed herein analyze the lightinformation to detect such a pattern. That is, examples disclosed hereinanalyze light information corresponding to a segment to determinewhether that particular segment includes a pattern having acharacteristic indicative of a person being present in a portion of theenvironment associated with that segment. Put another way, examplesdisclosed herein determine if a pulse-indicative pattern is present inthe light information corresponding to the segment. Examples disclosedherein record one or more aspects of the detected pulse-indicativepattern such as, for example, a rate (e.g., beats per minute) and/or oneor more brightness values (e.g., a maximum detected brightness of thesequence, a minimum detected brightness of the sequence, an averagevariance in brightness across the sequence, an average differencebetween peaks and values of the sequence, etc.). When a pulse-indicativepattern is detected in the light information associated with aparticular segment, examples disclosed herein determine whether nearbysegments also include a pulse-indicative pattern that is similar to thepattern detected in the particular segment (e.g., by comparing therecorded aspects or characteristics of the pattern, such as rate and/orone or more brightness values). Nearby segments or neighboring segmentsor proximate segments are those segments within a threshold distance ofeach other and/or within a threshold number of segments from each other.Examples disclosed herein determine that a person is present in an areacorresponding to the segment when a threshold number of proximatesegments (e.g., neighbors) include a similar (e.g., having a similarrate and/or being in sync) pulse-indicative pattern. As different peopleare unlikely to have the same pulse and even less likely to have thesame pulse synchronized together, examples disclosed herein can detectmultiple people in the environment. Notably, when an array of lightsensors is used to obtain the light information, examples disclosedherein are able to detect a number of people in the environment whilemaintaining the privacy of the people and without having to performcomputationally expensive image recognition algorithms. That is, in someexamples disclosed herein, the array of light sensors is incapable ofobtaining an identity of the detected people, thereby detected presenceof people in a unobtrusive manner,

FIG. 1 is an illustration of an example media exposure environment 100including an information presentation device 102, an example meter 104,and an audience 106 including a first person 108 and a second person110. In the illustrated example of FIG. 1, the information presentationdevice 102 is a television and the media exposure environment 100 is aroom of a household (e.g., a room in a home of a panelist such as thehome of a “Nielsen family”) that has been statistically selected todevelop television ratings data for population(s)/demographic(s) ofinterest. In the illustrated example of FIG. 1, one or more persons ofthe household have registered with an audience measurement entity (e.g.,by agreeing to be a panelist) and have provided demographic informationto the audience measurement entity as part of a registration process toenable associating demographics with viewing activities (e.g., mediaexposure). The example meter 104 of FIG. 1 can be implemented inadditional and/or alternative types of environments such as, forexample, a room in a non-statistically selected household, a theater, arestaurant, a tavern, a retail location, an arena, etc. In someexamples, the example meter 104 of FIG. 1 is implemented, at least inpart, in connection with additional and/or alternative types of mediapresentation devices such as, for example, a radio, a computer, a tablet(e.g., an iPad®), a cellular telephone, and/or any other communicationdevice able to present media to one or more individuals.

In some examples, the meter 104 of FIG. 1 is software installed inconsumer electronics associated with the environment 100 such as, forexample, a set-top box, a BluRay disc play, and/or a video game system(e.g., an XBOX® having a Kinect® sensor). In such instances, the examplemeter 104 of FIG. 1 is, for example, downloaded from a network,installed at the time of manufacture, installed via a port (e.g., auniversal serial bus (USB) port receiving a jump drive provided by theaudience measurement company), installed from a storage disc (e.g., anoptical disc such as a BluRay disc, Digital Versatile Disc (DVD) or CD(compact Disk), and/or any other suitable manner of installation.Executing the meter 104 on equipment associated with (e.g., owned by)one or more panelists is advantageous in that costs of installation arereduced by relieving the audience measurement entity of the need tosupply hardware to the monitored household. In some examples, the meter104 is integrated with the consumer electronics (e.g., by themanufacturer prior to sale to the consumer). That is, in some examples,the consumer electronics into which the meter 104 is integrated may berepurposed and/or data collected by the consumer electronics may berepurposed for audience measurement. For example, the meter 104 mayobtain light information (e.g., direct brightness values from an arrayof light sensors and/or brightness values extracted from image data)from one or more sensors of a video game system and/or may also collectsuch light information from internal sensor(s). In some examples, ratherthan installing the meter 104 on the consumer electronics of thepanelist(s), the example meter 104 of FIG. 1 is a dedicated audiencemeasurement unit provided by the audience measurement entity. In suchexamples, the meter 104 may include its own housing, processor, memoryand software to perform the desired audience measurement functions. Insome examples, the dedicated meter 104 is adapted to communicate with(e.g., via a wired and/or wireless connection), for example, informationcapturing devices implemented in the environment 100 such as, forexample, a video game system having image capturing equipment. In someexamples, the communications are effected via the consumer electronicsof the panelist (e.g., via a video game console). In some examples, themeter 104 includes information collection device(s), such as a lightsensor array, an image capturing device, and/or audio sensors and, thus,no direct interaction (outside of monitoring outputs) with the consumerelectronics owned by the panelist is involved. As disclosed below inconnection with FIGS. 2 and 3, the example meter 104 of FIG. 1 useslight information (e.g., brightness value sequences) representative ofthe environment 100 to detect people (e.g., the first and second persons108, 100). In some examples, the example meter 104 of FIG. 1 correlatesthe people detection information with media identifying informationcollected from the environment 100. In some examples, the correlation isdone by another device such as a remote data collection server. Thus,the example meter 104 of FIG. 1 generates audience measurement data(e.g., exposure information for particular media) for the environment100.

In the illustrated example of FIG. 1, the meter 104 is placed adjacentthe information presentation device 102 at a position for capturinglight information associated with the environment 100. While the meter104 is positioned beneath the information presentation device 102 in theexample of FIG. 1, the meter 104 can be located at alternative locations(e.g., above the information presentation device 102, mounted to a wallat a side of the information presentation device 102, etc.). The examplemeter 104 of FIG. 1 is a stationary apparatus in that the meter 104 ispositioned at a set location (e.g., on a shelf, on top of a mediacabinet, etc.) and meant to remain in that location when capturing lightinformation. That is, the example meter 104 of FIG. 1 is not meant formobile usage by, for example, picking up the meter 104 and capturingdata while moving the meter 104 around.

FIG. 2 illustrates an example implementation of the meter 104 of FIG. 1.The example meter 104 of FIG. 2 includes a person detector 200 to detectone or more persons in, for example, the media exposure environment 100of FIG. 1. The example person detector 200 of FIG. 2 obtains lightinformation associated with the media exposure environment via a lightsensor array 202 of the example meter 104. In some examples, the lightsensor array 202 is implemented within a housing of the meter 104(and/or a housing of an electronic component with which the meter 104 isintegrated, such as a video game console). Additionally oralternatively, the example light sensor array 202 may be a physicallyseparate component in communication with the example meter 104. In theillustrated example of FIG. 2, the light sensor array 202 is implementedby a horizontally arranged row of light sensors implemented behind alight guide (e.g., a lens). For example, the light sensor array 202 maybe a linear group of Contact Image Sensors (CISs) arranged behind alight guide (e.g., a lens). In such examples, the light guide focuseslight from a first portion of the environment 100 onto a first one ofthe sensors of the array 202, light from a second, different portion ofthe environment 100 onto a second one of the sensors of the array 202,light from a third, different portion of the environment 100 onto athird one of the sensors of the array 202, etc. In some examples, thelight gathered by individual ones of the sensors of the array 202represents a cumulative value corresponding to an aggregate of the lightpresent in the corresponding segment or region of the environment 100.As the sensors of the array 202 are arranged horizontally, the examplelight sensor array 202 provides light information for each of aplurality of substantially vertical (e.g., plus or minus one degree fromvertical) strips or segments of the environment 100 spanninghorizontally across a portion of the environment 100 to be monitored. Anexample implementation of the segments provided by the arrangement ofthe example light sensor array 202 is shown in FIG. 4 and discussedbelow in connection with FIG. 4.

The example meter 104 of FIG. 2 includes an image capturing device 204to capture, for example, two-dimensional image data representative ofthe media exposure environment 100 from which light information may beextracted. In some examples, the image capturing device 204 of FIG. 2 isimplemented within the housing of the meter 104 (and/or the housing ofan electronic component with which the meter 104 is integrated, such asa video game console). Additionally or alternatively, the example imagecapturing device 204 may be implemented as physically separate from thecomponent in which the meter 104 is implemented (e.g., the array may bea sensor such as a Kinnect® sensor of an XBOX® system). In someexamples, the meter 104 includes and/or communicates with only one ofthe light sensor array 202 and the image capturing device 204. Theexample image capturing device 204 of FIG. 2 is implemented by a sensorthat captures two-dimensional image data representative of theenvironment 100. In some examples, the two-dimensional sensor 506includes an infrared imager, a complimentary metal-oxide semiconductor(CMOS) camera, and/or a charge coupled device (CCD) camera. In someexamples, the image data captured by the image capturing device 204 isused to identify a person via, for example, any suitable facialrecognition technique and/or application.

The example meter 104 of FIG. 2 includes a thermal imaging device 205 togather temperature values of surfaces in the environment 100. In theillustrated example of FIG. 2, the thermal imaging device 205 isimplemented by any suitable device capable of determining a temperatureof a surface such as, for example, a forward looking infrared (FLIR)camera. The example thermal imaging device 205 of FIG. 2 detects, forexample, different magnitudes of thermal radiation emitted by, forexample, a person. Thus, the example thermal imaging device 205 detectsa first temperature of a surface of clothing and a second differenttemperature of a surface of human skin. In the example of FIG. 2, thethermal imaging device 205 is sensitive enough to detect a differencebetween the temperature of a surface of a person's face and thetemperature(s) of surfaces of the person's non-face body parts. Becausecertain blood vessels of the human face are closer to the surface of theskin compared to, for example, an arm or a leg, the surface of aperson's face is typically warmer than other surfaces of the body undersimilar conditions of the face (e.g., exposed to air or unclothed). Theexample thermal imaging device 205 of FIG. 2 has a resolution (e.g., 0.1degrees F.) great enough to detect these differences. Accordingly,measurements taken by the example thermal imaging device 205 of FIG. 2can be used to distinguish, for example, a face from a non-face bodypart.

In some examples, the light sensor array 202, the image capturing device204, and/or the thermal imaging device 205 only capture data when theinformation presentation device 102 is in an “on” state and/or when themeter 104 determines that media is being presented in the environment100 of FIG. 1.

As disclosed in detail below in connection with FIG. 3, the exampleperson detector 200 of FIG. 2 uses the obtained light information (e.g.,brightness sequence values) to generate a people count corresponding toa configurable time period (e.g., one minute intervals, thirty secondintervals, etc.) for the imaged portion of the example environment 100of FIG. 1. In particular, the example person detector 200 of FIG. 2breaks the example environment 100 of FIG. 1 into discrete segments(e.g., vertical strips) and determines whether individual ones of thesegments includes a pattern of light values over time corresponding toan effect that a pulse or heartbeat has on skin of a person. Put anotherway, the example person detector 200 of FIG. 2 uses light informationassociated with the environment 100 to detect pulse(s) of person(s)present in the environment 100. When the example person detector 200 ofFIG. 2 detects a pulse in one of the defined segments of the environment100, the example person detector 200 tests neighboring segments (e.g.,segments adjacent and/or proximate to each other within a threshold) todetermine whether the neighbor segments include a similar pattern. Thatis, the example person detector 200 of FIG. 2 determines whether thesame pulse is detected in neighboring segments. When a threshold numberof the neighboring segments include the same pulse-indicative pattern inthe respective light information, the example person detector 200determines that a person is present in the environment 100 at a locationcorresponding to the segments. In some examples, the threshold number ofneighboring segments corresponds to an expected size (e.g., a widthand/or other dimension) of a face, body, and/or body part. The number ofperson detections using the light information and the pulse-indicativepatterns for a particular period of time is referred to herein as thepulse-based person count for that period of time.

In some examples, the person detector 200 of FIG. 2 uses data collectedby the thermal imaging device 205 as a check or verification of thepulse-based person count. In some examples, the thermal imaging check orverification of the pulse-based person count is performed sporadically.For example, the thermal imaging check or verification of thepulse-based person count is performed according to a schedule (e.g.,periodically) and/or in response to a change in the pulse-based countfrom one period of time to another period of time (e.g., when a personenters or leaves the environment 100). As described in detail below, thedata provided by the thermal imaging device 205 is useful in verifyingthat, for example, the pulse-based person count does not includemultiple person detections for a single person due to, for example, anon-face body part being mistaken for a face. That is, the pulse-basedperson count may include more than one person detection for a singleperson, which would inaccurately represent the actual number of peoplein the environment 100. As described below, the example person detector200 of FIG. 2 uses the thermal imaging device 205 to ensure that such adouble counting does not occur. When the example person detector 200 ofFIG. 2 determines that the pulse-based person count is inaccurate (e.g.,by determining that one of the pulse-based person detections correspondsto a hand rather than face), the example person detector 200 of FIG. 2adjusts the pulse-based person count accordingly (e.g., by reducing thepulse-based person count). Additionally or alternatively, the dataprovided by the example thermal imaging device 205 may indicate that thepulse-based person count is less than an actual number of people presentin the environment 100. For example, the number of faces detected in thedata provided by the example thermal imaging device 205 may be greaterthan the pulse-based person count. In such instances, the example persondetector 200 of FIG. 2 adjusts the pulse-based person count accordinglyand, in some examples, marks the pulse-based person count as adjusted bythe check or verification provided by the thermal imaging device 205. Anexample implementation of the example person detector 200 of FIG. 2 isdisclosed below in connection with FIGS. 3 and 4.

The example person detector 200 of FIG. 2 outputs, for example,calculated people counts or tallies to the example time stamper 206. Insome examples, for certain periods of time the outputted people countsor tallies correspond directly to the pulse-based person count, whilefor other periods of time the outputted people counts or talliescorrespond to an adjusted pulse-based person count that has beenadjusted according to the data provided by the thermal imaging device205. The time stamper 206 of the illustrated example includes a clockand a calendar. The example time stamper 206 associates a time period(e.g., 1:00 a.m. Central Standard Time (CST) to 1:01 a.m. CST) and date(e.g., Jan. 1, 2014) with each calculated people count by, for example,appending the period of time and date information to an end of thepeople data. In some examples, the time stamper 206 applies a singletime and date rather than a period of time. A data package (e.g., thepeople count, the time stamp, the image data, etc.) is stored in memory208 of the meter 104. The example memory 208 of FIG. 2 may include avolatile memory (e.g., Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM, etc.) and/or a non-volatile memory (e.g., flash memory). Theexample memory 208 of FIG. 2 may also include one or more mass storagedevices such as, for example, hard drive disk(s), compact disk drive(s),digital versatile disk drive(s), etc. When the example meter 104 isintegrated into, for example, a video game system or a set-top box, themeter 104 may utilize memory of the video game system or the set-top boxto store information such as, for example, the people counts, the imagedata, etc.

The example time stamper 206 of FIG. 2 also receives data from anexample media detector 210 of the example meter 104. The example mediadetector 210 of FIG. 2 detects presentation(s) of media in the mediaexposure environment 100 and/or collects identification informationassociated with the detected presentation(s). For example, the mediadetector 210 of FIG. 2, which may be in wired and/or wirelesscommunication with the information presentation device 102 (e.g., atelevision), a video game system deployed in the environment 100, an STBassociated with the information presentation device 102, and/or anyother component of FIG. 1, can identify a presentation time and/or asource of a presentation. The presentation time and the sourceidentification data (e.g., channel identification data) may be utilizedto identify the program by, for example, cross-referencing a programguide configured, for example, as a look up table. In such instances,the source identification data is, for example, the identity of achannel (e.g., obtained by monitoring a tuner of an STB or a digitalselection made via a remote control signal) currently being presented onthe information presentation device 102.

Additionally or alternatively, the example media detector 210 of FIG. 2can identify the presentation by detecting codes and/or watermarksembedded with or otherwise conveyed (e.g., broadcast) with media beingpresented via an STB and/or the information presentation device 102. Asused herein, a code is an identifier that is transmitted with the mediafor the purpose of identifying (e.g., an audience measurement code)and/or for tuning to (e.g., a packet identifier (PID) header and/orother data used to tune or select packets in a multiplexed stream ofpackets) the corresponding media. Codes may be carried in the audio, inthe video, in metadata, in a vertical blanking interval, in a programguide, in content data, or in any other portion of the media and/or thesignal carrying the media. In the illustrated example of FIG. 2, themedia detector 210 extracts the code(s) from the media. In otherexamples, the media detector may collect samples of the media and exportthe samples to a remote site for detection of the code(s).

Additionally or alternatively, the example media detector 210 of FIG. 2can collect a signature to identify the media. As used herein, asignature is a representation of a characteristic of the signal carryingor representing one or more aspects of the media (e.g., a frequencyspectrum of an audio signal). Signatures may be thought of asfingerprints of the media. Collected signature(s) can be comparedagainst a collection of reference signatures of known media (e.g.,content and/or advertisements) to identify tuned media. In someexamples, the signature(s) are generated by the media detector 210.Additionally or alternatively, the example media detector 210 of FIG. 2collects samples of the media and exports the samples to a remote sitefor generation of the signature(s). In the example of FIG. 2,irrespective of the manner in which the media of the presentation isidentified (e.g., based on tuning data, metadata, codes, watermarks,and/or signatures), the media identification information is time stampedby the time stamper 206 and stored in the memory 208.

In the illustrated example of FIG. 2, an output device 212 periodicallyand/or aperiodically exports the people information and/or the mediaidentification information from the memory 208 to a data collectionfacility 214 via a network (e.g., a local-area network, a wide-areanetwork, a metropolitan-area network, the Internet, a digital subscriberline (DSL) network, a cable network, a power line network, a wirelesscommunication network, a wireless mobile phone network, a Wi-Fi network,etc.). In some examples, the example meter 104 utilizes thecommunication capabilities (e.g., network connections) of a video gamesystem and/or a set-top box to convey information to, for example, thedata collection facility 214. In the illustrated example of FIG. 2, thedata collection facility 214 is managed and/or owned by an audiencemeasurement entity (e.g., The Nielsen Company (US), LLC). The audiencemeasurement entity associated with the example data collection facility214 of FIG. 2 utilizes the people tallies generated by the persondetector 200 in conjunction with the media identifying data collected bythe media detector 210 to generate exposure information. The informationfrom many panelist locations may be collected and analyzed to generateratings representative of media exposure by one or more populations ofinterest.

While an example manner of implementing the meter 104 of FIG. 1 isillustrated in FIG. 2, one or more of the elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample person detector 200, the light sensor array 202, the exampleimage capturing device 204, the example time stamper 206, the examplemedia detector 210, the example output device 212, and/or, moregenerally, the example meter 104 of FIG. 2 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example persondetector 200, the light sensor array 202, the example image capturingdevice 204, the example time stamper 206, the example media detector210, the example output device 212, and/or, more generally, the examplemeter 104 of FIG. 2 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example person detector 200, the light sensor array 202, the exampleimage capturing device 204, the example time stamper 206, the examplemedia detector 210, the example output device 212, and/or, moregenerally, the example meter 104 of FIG. 2 is/are hereby expresslydefined to include a tangible computer readable storage device orstorage disk such as a memory, a digital versatile disk (DVD), a compactdisk (CD), a Blu-ray disk, etc. storing the software and/or firmware.Further still, the example meter 104 of FIG. 2 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

FIG. 3 illustrates an example implementation of the example persondetector 200 of FIG. 2. The example person detector 200 of FIG. 3includes a segment definer 300 to define a plurality of segments eachcorresponding to a portion of the monitored area of the example mediaexposure environment 100. In the illustrated example of FIG. 3, thesegment definer 300 is programmable by, for example, an administrator orconfiguration personnel associated with the example meter 104 (e.g., aprogrammer of the audience measurement entity associated with theexample data collection facility 214 of FIG. 2). The example segmentdefiner 300 of FIG. 3 defines a number and size of the segments. In someexamples, one or more of the segments defined by the example segmentdefiner 300 differ in size (e.g., width). In some examples, the segmentsdefined by the example segment definer 300 are the same size.

FIG. 4 illustrates an example segmentation 400 of the monitored area ofthe example media exposure environment 100 of FIG. 1 implemented by theexample segment definer 300 of FIG. 3. The example segmentation 400 ofFIG. 4 includes a plurality of segments 402 a-402 zz having the samewidth. In the illustrated example, when the example person detector 200of FIG. 3 is using light information provided by the example lightsensor array 202 of FIG. 2 to detect a human pulse, the example segmentdefiner 300 of FIG. 3 defines each of the segments to correspond to oneof or a group of the sensors of the array 300. In the illustratedexample, when the example person detector 200 of FIG. 3 is using lightinformation extracted from image data provided by the example imagecapturing device 204 to detect a human pulse, the example segmentdefiner 300 of FIG. 3 defines each of the segments based on, forexample, a resolution of the image capturing device 204. For example,when the image capturing device 204 captures images of a firstresolution, the example segment definer 300 of FIG. 3 defines a firstnumber of segments of a first size. When the image capturing device 204captures images of a second resolution greater than the firstresolution, the example segment definer 300 of FIG. 3 defines a secondnumber of segments greater than the first number of segments of a secondsize less than the first size.

The example person detector 200 of FIG. 3 includes a pulse identifier302 that analyzes obtained light information (e.g., brightness valuesequences provided by the light sensor array 202 or brightness valuesequences extracted from image data provided by the image capturingdevice 204) for the segments defined by the example segment definer 300to determine whether the light information over a period of time isindicative of a human pulse. The example pulse identifier 302 of FIG. 3takes advantage of changes in human skin that result from pulse-drivenblood flow. That is, as blood flows through a person, light informationassociated with the skin of the person, especially the skin of a face,varies over time in accordance with a pulse of the person. For example,a brightness value associated with the face of person detected by, forexample, one of the sensors of the light sensor array 202 of FIG. 2, mayvary in a repeated fashion corresponding to the person's pulse rate. Theexample pulse identifier 302 of FIG. 3 analyzes the obtained lightinformation to detect occurrences of repeated variations or patternedshapes in brightness values. In some examples, when the pulse identifier302 detects a pattern in the light information, the example pulseidentifier 302 determines whether the pattern is similar (e.g., within athreshold similarity) to a human pulse pattern in terms of, for example,beats per minute. Human beings typically have pulses that fall within arange of expected rates (e.g., beats per minute), especially when peopleare at rest. Thus, the example pulse identifier 302 of FIG. 3 looks forpatterns corresponding to typical, expected pulse rates (e.g., within arange of expected pulse rates of persons exposed to media). In someexamples, the pulse identifier 302 of FIG. 3 compares detected patternsin the light information to reference patterns that are known tocorrespond to a human pulse. In some examples, the pulse identifier 302utilizes one or more filters and/or processing techniques to eliminatebrightness variations resulting from non-pulse sources such as, forexample, a display of the information presentation device 102.

When the example pulse identifier 302 of FIG. 3 detects a patternindicative of a human pulse, the example pulse identifier 302 stores thecorresponding information in a collection of pulse detections 304. Theexample pulse detections 304 are stored in any suitable data structuresuch as, for example, a database. In the illustrated example of FIG. 3,each of the example pulse detections 304 includes data indicative of,for example, the beats per minute, an identifier of the segment in whichthe pulse was detected, brightness values detected over the period oftime, a maximum brightness value for the period of time, a minimumbrightness value for the period of time, an average difference betweenpeaks of the detected pattern, a time stamp associated with the periodof time over which the pulse pattern was detected, and/or any othersuitable information.

The example person detector 200 of FIG. 3 includes a group identifier306 to identify one or more groups of proximate pulse-indicativesegments that have similar light variation patterns. As the segmentsused by the example person detector 200 typically have a width muchsmaller than the width of a normal human face, the example groupidentifier 306 of FIG. 3 detects groupings of the segments within athreshold distance from each other having one or more similarcharacteristics (e.g., beats per minute, maximum brightness value,minimum brightness value, average differences in brightness peaks andvalleys, etc.). The example group identifier 306 thus determines whethera threshold number of neighboring or proximate pulse-indicative segmentsinclude similar characteristics with respect to the pulse informationstored in the corresponding ones of the pulse detections 304 of FIG. 3.In some examples, the threshold number of proximate pulse-indicativesegments having similar characteristic(s) is adjustable and correspondsto an expected number of segments that would correspond to a body, humanface, or another body part (e.g., depending on the size of the segmentsdefined by the example segment definer 300). In some examples, theexample group identifier 306 requires the proximate segments to havepulses that are in synchronization to conclude that the correspondingpulse belongs to the same person. That is, if the detected pulses indifferent segments belong to the same person, those pulses aresynchronized. Therefore, the example group identifier 306 compares thetiming information of the pulse detections 304 to determine whether thecorresponding pulses exhibit synchronicity. If the example groupidentifier 306 of FIG. 3 identifies at least a threshold number ofneighboring or proximate segments including a pulse pattern havingsimilar characteristic(s) (e.g., beats per minute) and exhibitingsynchronicity, the example group identifier 306 designates thosesegments as corresponding to a person.

Example identifications made by the example group identifier 306 of FIG.3 are shown in FIG. 4. In the illustrated example, the example pulseidentifier 302 has detected a first pulse-indicative pattern 404 oflight information in a first plurality of the segments 406. The examplegroup identifier 306 of FIG. 3 has determined that the first pluralityof segments 406 have similar light characteristics such as, for example,the beats per minute of the first example pattern 404. Thus, the examplegroup identifier 306 determines that a first person is in theenvironment 100 at a location associated with the first plurality ofsegments. The example group identifier 306 of FIG. 3 makes similardeterminations and identifications in connection with second, third andfourth detected pulse patterns 408-412 and corresponding second, thirdand fourth pluralities of segments 414-418.

The example person detector 200 of FIG. 3 includes a spacing tester 308to check the identifications generated by the example group identifier306 for one or more spacing requirements. For example, two peoplelocated close together may have similar pulse patterns in sync that thegroup identifier 306 may mistake for a single person. While instances oftwo different people having similar pulse patterns, as well as havingsynchronized pulse patterns are rare, the example spacing tester 308 ofFIG. 3 determines whether such a circumstance exists. To do so, theexample spacing tester 308 of FIG. 3 determines if the number ofsegments belonging to a single group identified by the example groupidentifier exceeds a threshold. In the illustrated example, thethreshold applied by the example spacing tester 308 corresponds to anumber of segments unlikely to correspond to only a single human face.The number of segments in the threshold utilized by the example spacingtester 308 is, for example, a function (e.g., a percentage, such as fivepercent) of a total number of segments defined by the segment definer300. The number of segments in the threshold utilized by the examplespacing tester 308 may be determined and/or expressed in any additionalor alternative manner (e.g., selected based on a distance between anexpected person detection area and the light sensor array 202 and/or theimage capturing device 204). When a group identification generated bythe example group identifier 306 of FIG. 3 includes a number of segmentsin excess of the threshold applied by the example spacing tester 308,the example spacing tester 308 causes the single group identification tobe broken or split into two separate group identifications. When a groupidentification generated by the example group identifier 306 of FIG. 3includes a number of segments below the threshold applied by the examplespacing tester 308, the single group identification is confirmed asindicative of one person. Each of the detections confirmed by theexample spacing tester 308 is stored as a pulse-based person detection310. Any suitable information associated with the individual pulse-basedperson detections 310 is stored in connection with the pulse-baseddetections 310 including, for example, a time stamp, the correspondinglight information, the corresponding pulse pattern, etc. In theillustrated example of FIG. 2, the stored information includescoordinates indicative of a location in the environment 100 at which thecorresponding pulse-based person detection occurred. The coordinateinformation includes, for example, which of the segments defined by theexample segment definer 300 were part of the corresponding pulse-basedperson detection. Ones of the example pulse-based person detections 310of FIG. 3 captured during a same period of time (e.g., according to atime stamp) can be added together to generate a pulse-based personcount.

The example person detector 200 of FIG. 2 includes a thermal faceidentifier 312, a comparator 314, an adjustor 316, and a thermalnon-face identifier 318 to perform a check or verification on theexample pulse-based detections 310. In some examples, the thermalverification on the example pulse-based detections 310 is performedperiodically (e.g., in response to a timer. Additionally oralternatively, the thermal verification may be performed aperiodically(e.g., in response to an event in the environment 100, such as a changein the pulse-based person count (e.g., due to a person leaving orentering the environment 100)). Alternatively, the example persondetector 200 of FIG. 2 can be set to omit or forego the thermalverification of the pulse-based person detections 310.

The example thermal face identifier 312 of FIG. 3 utilizes data providedby the example thermal imaging device 205 to detect human faces in theenvironment 100. A typical (e.g., healthy) person has a temperature onthe surface of their face above a particular temperature (e.g., abovethirty-four degrees Celsius) or within a particular temperature range(e.g., between 34 degrees Celsius and 37 degrees Celsius). The examplethermal face identifier 312 of FIG. 3 analyzes the thermal images todetermine locations in the environment 100 corresponding to theparticular temperature. FIG. 5 is an illustration of a thermal imageprovided by the example thermal imaging device 205 of FIG. 2corresponding to the example of FIG. 4. In the example of FIG. 5, thethermal face identifier 312 detects first, second, third and fourthfaces 500-506, respectively, due to the high temperature readings at thecorresponding locations relative to the remainder of image.

The example comparator 314 of FIG. 3 receives location data associatedwith the thermally-detected faces 500-506 and compares the receivedinformation with the location data associated with individual one(s) ofthe pulse-based person detections 310. In the illustrated example, thelocation data associated with the individual ones of thethermally-detected faces 500-506 includes a coordinate (e.g., an X-Ycoordinate). As the pulse-based person detections 310 have locationscorresponding to the vertical segments defined by the segment definer200, the example comparator 314 uses the horizontal component of thecoordinate (e.g., the X-component) of the thermally-detected faces500-506 when comparing the locations of the pulse-based persondetections 310 and the thermally-detected faces 500-506. That is, theexample comparator 314 of FIG. 3 determines whether each of thelocations of the pulse-based person detections 310 has a counterpartthermally-detected face. If the example comparator 314 of FIG. 3determines that each of the locations of the pulse-based persondetections 310 match (e.g., with a threshold) one of the locations ofthe thermally-detected faces 500-506 (i.e., one-for-one correspondence),the example adjustor 316 does not adjust the pulse-based detections 310.Rather, the example adjustor 316 designates the pulse-based detections310 as verified. In some examples, if the comparator 314 determines thata mismatch exits between the number of thermally-detected faces 500-506and the number of the example pulse-based detections 310, the exampleadjustor 316 either marks mismatching one(s) of the pulse-based persondetections 310 as unverified or eliminates (e.g., discards or deletes)the mismatching one(s) of the pulse-based person detections 310.Additionally or alternatively, the example adjustor 316 can incrementthe pulse-based person count when the thermally-detected faces 500-506include a face not present in the example pulse-based person detections310.

The example thermal non-face identifier 318 of FIG. 2 utilizes dataprovided by the example thermal imaging device 205 to detect non-facebody parts in the environment 100. The one or more of the pulse-basedperson detections 310 may have been based on a non-face body part and,thus, may have skewed the stored pulse-based person count. A typical(e.g., healthy) person has a temperature on the surface of theirnon-face body parts above a particular temperature (e.g., 31 degreesCelsius) but less than the corresponding face temperature (e.g., 34degrees Celsius). The example thermal non-face identifier 318 of FIG. 3analyzes the thermal images to determine locations in the environment100 corresponding to the non-face temperature(s) (e.g., between 30degrees Celsius and 33 degrees Celsius). In the example of FIG. 5, theexample thermal non-face identifier 318 detects a plurality of non-facebody parts 508-526.

The example comparator 314 of FIG. 3 receives location data associatedwith the thermally-detected non-face body parts 508-526 and compares thereceived information with the location data associated with individualone(s) of the pulse-based person detections 310. In the illustratedexample, the location data associated with the individual ones of thethermally-detected non-face body parts 508-526 includes a coordinate(e.g., an X-Y coordinate). As the pulse-based person detections 310 havelocations corresponding to the vertical segments defined by the segmentdefiner 200, the example comparator 314 uses the horizontal component ofthe coordinate (e.g., the X-component) of the thermally-detectednon-face body parts 508-526 when comparing the locations of thepulse-based person detections 310 and the thermally-detected faces500-506. The example comparator 314 of FIG. 3 determines whether any ofthe locations of the pulse-based person detections 310 correspond to thelocation of any of the thermally-detected non-face body parts 508-526.The example comparator 314 of FIG. 3 determining that the location ofone of the pulse-based person detections 310 substantially matches(e.g., with a threshold (e.g., plus or minus one-tenth of a degree) thelocation of one of the thermally-detected non-face body parts 508-526may indicate that the pulse identifier 302 mistook a non-face body partfor a face. However, because a vertical segment of the environment 100may include a face and a non-face body part (e.g., the face 502 and thenon-face body part 514 of FIG. 5), the example comparator 314 referencesthe thermally-detected faces 500-508 to determine whether athermally-detected face is also at that location. If nothermally-detected face is present at the shared location of thepulse-based person detection 310 and the thermally-detected non-facebody part, the example adjustor 316 discards the corresponding one(s) ofthe pulse-based person detections 310. Thus, in the example of FIG. 3,the example thermal face identifier 312, the example comparator 314, theexample adjustor 316, and the example thermal non-face identifier 318cooperate to make one or more adjustments to the example pulse-basedperson detections 310 to determine people counts for respective periodsof time.

In the illustrated example of FIG. 3, the example person detector 200outputs a number of distinct group identifications as a detected numberof people for the corresponding period of time. For example, the exampleperson detector 200 of FIG. 3 sends the detected number of people to theexample time stamper 206 of FIG. 2. In some instances, when the thermalverification is not performed, the output people count correspondsdirectly to the pulse-based person count of the pulse-based persondetections 310. In some instances, the output people count correspondsto the pulse-based person count of the pulse-based person detections 310as adjusted by the example adjustor 316.

While an example manner of implementing the person detector 200 of FIG.2 is illustrated in FIG. 3, one or more of the elements, processesand/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example segment definer 300, the example pulse identifier302, the example group identifier 306, the example spacing tester 308,the example thermal face identifier 312, the example comparator 314, theexample adjustor 316, the example thermal non-face identifier 318,and/or, more generally, the example person detector 200 of FIG. 3 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample segment definer 300, the example pulse identifier 302, theexample group identifier 306, the example spacing tester 308, theexample thermal face identifier 312, the example comparator 314, theexample adjustor 316, the example thermal non-face identifier 318,and/or, more generally, the example person detector 200 of FIG. 3 couldbe implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example, segmentdefiner 300, the example pulse identifier 302, the example groupidentifier 306, the example spacing tester 308, the example thermal faceidentifier 312, the example comparator 314, the example adjustor 316,the example thermal non-face identifier 318, and/or, more generally, theexample person detector 200 of FIG. 3 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example person detector of FIG. 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 3, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example person detector 200 of FIGS. 2 and/or 3 areshown in FIGS. 6 and 7. In this example, the machine readableinstructions comprise a program for execution by a processor such as theprocessor 812 shown in the example processor platform 800 discussedbelow in connection with FIG. 8. The program may be embodied in softwarestored on a tangible computer readable storage medium such as a CD-ROM,a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 812, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 812 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 6 and 7, many othermethods of implementing the example person detector 200 of FIGS. 2and/or 3 may alternatively be used. For example, the order of executionof the blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined.

As mentioned above, the example processes of FIGS. 6 and 7 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIG. 5 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The example of FIG. 6 begins at block 600 with an initialization of theexample person detector 200 of FIGS. 2 and/or 3 (block 600). In theillustrated example, the person detector 200 is initialized and/orremains active when the example media detector 210 of FIG. 2 determinesthat media is being presented in the example media exposure environment100 of FIG. 1. In some examples, the person detector 200 is initializedand/or remains active when the example information presentation device102 is turned on. In some examples, the person detector 200 remains onirrespective of whether media is being presented. To monitor a portionof the example media exposure environment 100, the example persondetector 200 obtains light information representative of the portion ofthe media exposure environment 100 (block 602). In the illustratedexample, the light information utilized by the example person detector200 includes light information provided by the example light sensorarray 202 of FIG. 2 (e.g., aggregate brightness values for individualones of the segments) and/or light information provided by the exampleimage capturing device 204 of FIG. 2 (e.g., brightness values extractedfrom two-dimensional image data). In the illustrated example, the persondetector 200 obtains light information for each of the plurality ofsegments defined by the example segment definer 300 of FIG. 3. Theexample pulse identifier 302 of FIG. 3 analyzes the light informationassociated with individual ones of the segments to determine whether thelight information of any of the segments includes a pulse-indicativepattern (block 604). For example, the pulse identifier 302 determineswhether a pattern is found in the light information corresponding to arepetitive brightness variance on skin of a person resulting frompulse-driven blood flow. If a pulse-indicative pattern is detected in asegment by the pulse identifier 302 (block 604), the example pulseidentifier 302 determines that the particular segment is a candidateindicator of a person being present at a location in the media exposureenvironment 100 associated with the particular segment. The examplepulse identifier 302 records the information associated with theidentification of the pulse-indicative pattern in the pulse detections304.

The example group identifier 306 of FIG. 3 analyzes the pulse detections304 for a period of time to identify neighboring or proximate (e.g.,within a threshold distance from each other) segments having apulse-indicative pattern (block 606). Further, the example groupidentifier 306 determines whether a same or similar pulse-indicativepattern was detected in a threshold number of the neighboring orproximate segments (block 608). Finding a grouping of segments includingsynchronized, similar pulse-indicative patterns is an indication of aperson being present in the media exposure environment 100. In someexamples, one or more characteristics (e.g., beats per minute,brightness value(s), etc.) of the pulse-indicative patterns detected inneighboring or proximate segments are compared to each other and asimilarity is detected when the characteristic(s) are within a threshold(e.g., percentage) similarity. The groups of segments having similarcharacteristic(s) are tagged as indicative of the presence of a person(block 610).

In the illustrated example, the spacing tester 308 of FIG. 3 applies oneor more spacing requirements to the identified groups of segments todetermine whether any of the group(s) exceeds a threshold (block 612).For example, the spacing requirement(s) include a threshold span that agroup may have to account for instances in which two people are detectedas one person by the example group identifier 306. If a group ofsegments does not meet the spacing requirements, the example spacingtester 308 splits the violating group into two person indications. Theexample spacing tester 308 stores each of the distinct groups as apulse-based person detection 310 for the corresponding period of time(block 614).

In the example of FIG. 6, the person detector 200 determines whether thethermal verification is to be executed (block 616). In some examples,the person detector 200 maintains a schedule for the thermalverification. Additionally or alternatively, the thermal verification isto be executed when a change is detected in the environment 100according to, for example, the pulse-based person count. If the thermalverification is not to be executed (block 616), the example persondetector 200 outputs the sum of the pulse-based person detections 310for the current period of time as the people count for the currentperiod of time (block 618). If the thermal verification is to beexecuted (block 616), control passes to FIG. 7 which is described below.As the thermal verification may result in an adjustment to thepulse-based person detections 310, the example person detector 200outputs the pulse-based person count as adjusted (if any) by the exampleadjuster 316 of FIG. 3 (block 620). Control passes to block 602 wherelight information for another period of time is obtained.

FIG. 7 begins with initialization of the thermal imaging device 205 inresponse to the example person detector 200 determining that the thermalverification of the pulse-based person count is to be executed (block700). In the example of FIG. 7, the thermal imaging device 205 capturesa thermal image of the environment 100 (block 702). The example thermalimaging device 205 detects temperatures of surfaces in the environmentto generate the thermal image, such as the example thermal image of FIG.5. In the illustrated example, the resolution of the thermal imagingdevice 205 is great enough to distinguish between a face and a non-facebody part that is typically cooler at the surface than a face.

In the example of FIG. 7, the thermal face identifier 312 of FIG. 3identifies location(s) in the thermal image that include a face (block704). For example, the thermal face identifier 312 determines whichlocation(s) in the thermal image indicate that a surface having atypical temperature of a human face is present. Further, in the exampleof FIG. 7, the thermal non-face identifier 318 identifies location(s) inthe thermal image that include a non-face body part (block 706). Forexample, the thermal non-face identifier 318 determines whichlocation(s) in the thermal image indicate that a surface having atypical temperature of a human non-face body part is present. Thetypical temperature of a surface of a non-face body part is lower than atemperature of a surface of a face. The example thermal face identifier312 and the example thermal non-face identifier 318 distinguish betweenthe face(s) and the non-face body part(s) and store informationassociated with each, such as a location and temperature data.

In the example of FIG. 7, the comparator 314 of the person detector 200uses the thermally-detected faces (e.g., the faces 500-506 of FIG. 5) toverify the pulse-based person detections 310 (block 708). For example,the comparator 314 compares the locations of the faces detected by thethermal face identifier 312 to the locations of the pulse-based persondetections 310. If one of the pulse-based person detections 310corresponds to one of the locations of the thermally-detected faces,that one of the pulse-based person detections 310 is verified asaccurate. Additionally or alternatively, if one of the pulse-basedperson detections 310 does not correspond with one of the locations ofthe thermally-detected faces, that one of the pulse-based persondetections 310 is, for example, marked as unverified (e.g., stillincluded in the pulse-based person count but differently marked orcategorized). Alternatively, in some examples, the pulse-based persondetection(s) 310 not corresponding to any of the locations of thethermally-detected faces is discarded (e.g., no longer included in thepulse-based person count). Additionally or alternatively, if thelocation of a thermally-detected face does not correspond to any of thepulse-based person detections 310, the pulse-based person count isincremented to account for the face missed by the pulse identifier 302,the group identifier 306, and/or the spacing tester 308. If more thanone face is so missed the count is incremented accordingly to accountfor each missed face.

In the example of FIG. 7, the comparator 314 of the person detector 200uses the thermally-detected non-face body parts (e.g., the non-face bodyparts 508-526 of FIG. 5) to identify false positives in the pulse-basedperson detections 310 (block 710). Under certain circumstances, theexample pulse-identifier 302 may mistake a non-face body part (e.g., ahand) as a face. As discussed above, the example thermal non-faceidentifier 318 identifies locations in the environment 100 correspondingto a non-face body part. Thus, the example comparator 314 determineswhether any of the locations of the pulse-based person detections 310correspond to any of the locations of the thermally-detected non-facebody parts. Further, to account for instances in which a segment of theenvironment 100 (e.g., a vertical segment) includes a thermally-detectedface and a thermally-detected non-face body part, the comparator 314references the thermally-detected face locations to determine whetherthe pulse-based person detection 310 should not be discarded. That is,if the location of the pulse-based person detection 310 corresponds tothe location of a thermally-detected non-face body part and the locationof a thermally-detected face, that pulse-based person detection 310 isnot discarded. On the other hand, those of the pulse-based persondetections 310 having a location corresponding to one of thethermally-detected non-face body parts and not one of thethermally-detected faces are discarded as false positives.

In the example of FIG. 7, the adjuster 316 makes the adjustments (e.g.,verifications, discards, increments, etc.) to the pulse-based persondetections based on the thermal imaging detections (block 712). Theresult of the adjustments at block 712 form the adjusted pulse-basedperson count of block 620 of FIG. 6. Control then returns to FIG. 6(block 714).

FIG. 8 is a block diagram of an example processor platform 800 capableof executing the instructions of FIGS. 6 and/or 7 to implement theperson detector 200 of FIGS. 2 and/or 3. The processor platform 800 canbe, for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, or any other type of computingdevice.

The processor platform 800 of the illustrated example includes aprocessor 812. The processor 812 of the illustrated example is hardware.For example, the processor 812 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 812 of the illustrated example includes a local memory 813(e.g., a cache). The processor 812 of the illustrated example is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a bus 818. The volatile memory 814 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 816 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 814, 816 is controlledby a memory controller.

The processor platform 800 of the illustrated example also includes aninterface circuit 820. The interface circuit 820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 822 are connectedto the interface circuit 820. The input device(s) 822 permit(s) a userto enter data and commands into the processor 812. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 824 are also connected to the interfacecircuit 820 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 820 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network826 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 828 for storing software and/or data.Examples of such mass storage devices 828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 832 of FIGS. 6 and/or 7 may be stored in the massstorage device 828, in the volatile memory 814, in the non-volatilememory 816, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

Examples disclosed herein detect people in an environment in anefficient, accurate and private manner that avoids drawbacks of knownimage data recognition systems. For example, by detecting people usinglight information and pulse-indicative patterns in the lightinformation, examples disclosed herein can detect people whilemaintaining the privacy (e.g., identity) of the person. That is, peopleare detected by examples disclosed herein in a less intrusive and lessobtrusive manner than, for example, taking a picture of the environment.Further, light sensors (e.g., an array of light sensors) used byexamples disclosed herein are inexpensive relative to certain imagecapturing equipment.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus, comprising: a receiver to obtainrespective light information for a plurality of segments of an image,the segments corresponding to respective portions of an environment; apulse identifier to detect a first human pulse waveform in the lightinformation associated with a first one of the segments and to detect asecond human pulse waveform in the light information associated with asecond one of the segments, the first human pulse waveform notsynchronized with the second human pulse waveform; a grouper todetermine whether the light information associated with a thresholdamount of the segments proximate the first one of the segments includesthe first human pulse waveform identified in the first one of thesegments, and to generate a first presence indication when the lightinformation associated with the threshold amount of the segmentsproximate the first segment includes the first human pulse waveform; andthe grouper to determine whether the light information associated with athreshold amount of the segments proximate the second one of thesegments includes the second human pulse waveform identified in thesecond one of the segments, and to generate a second presence indicationwhen the light information associated with the threshold amount of thesegments proximate the second segment includes the second human pulsewaveform.
 2. The apparatus as defined in claim 1, further including atime stamper to associate media identifying information collected fromthe environment with the first presence indication.
 3. The apparatus asdefined in claim 1, wherein the threshold amount of the segmentscorresponds to a size of a human face.
 4. The apparatus as defined inclaim 1, further includes a spacing tester to determine whether a numberof the segments proximate the first one of the segments including thefirst human pulse waveform exceeds a second threshold.
 5. The apparatusas defined in claim 4, wherein the spacing tester is to, when the numberof the segments proximate the first one of the segments including thefirst human pulse waveform exceeds the second threshold, indicate apresence of more than one person.
 6. An apparatus as defined in claim 1,wherein the light information is provided by an array of light sensorsincapable of identifying detected persons.
 7. The apparatus as definedin claim 1, wherein the threshold amount corresponds to a size of a bodypart.
 8. The apparatus as defined in claim 1, further including a faceidentifier to identify a first location of a face in the environmentusing thermal imaging data.
 9. The apparatus as defined in claim 8,further including a comparator to compare the first location of the faceto a second location associated with the first presence indication. 10.The apparatus as defined in claim 8, further including an adjustor toadjust a people count when the first location of the face does notcorrespond to a second location of the first presence indication. 11.The apparatus as defined in claim 1, further including a non-face bodypart identifier to detect a first location of a non-face body part inthe environment using thermal imaging data.
 12. The apparatus as definedin claim 11, further including a comparator to compare the firstlocation of the non-face body part to a second location associated withthe first presence indication.
 13. The apparatus as defined in claim 11,further including an adjuster to discard the person indication when thefirst location is similar to a second location associated with the firstpresence indication.
 14. A method, comprising: obtaining brightnessvalue sequences respectively corresponding to portions of anenvironment; detecting, with a processor, a first one of the brightnessvalue sequences that exhibits a first waveform indicative of a firsthuman pulse and a second one of the brightness value sequences thatexhibits a second waveform indicative of a second human pulse, thesecond human pulse not synchronized with the first human pulse;identifying, with the processor, a first group of the brightness valuesequences proximate the first portion and a second group of thebrightness value sequences proximate the second portion; determining,with the processor, whether a threshold amount of the first identifiedgroup of the brightness value sequences exhibit the first waveform andwhether a threshold amount of the second identified group of thebrightness value sequences exhibit the second waveform; and indicating,with the processor, a presence of a first person in the environment whenthe threshold amount of the first identified group of the brightnessvalue sequences exhibit the first waveform and a presence of a secondperson in the environment when a threshold amount of the secondidentified group of the brightness value sequences exhibit the secondwaveform.
 15. The method as defined in claim 14, further includingassociating the presence of the first person with media identifyinginformation detected in the environment at a time corresponding tocapture of the brightness value sequences.
 16. The method as defined inclaim 14, further including determining whether the first identifiedgroup of the brightness value sequences that exhibit the first waveformexceeds a second threshold.
 17. The method as defined in claim 16,further including, when the first identified group of the brightnessvalue sequences that exhibit the first waveform exceeds the secondthreshold, indicating a presence of more than one person.
 18. The methodas defined in claim 14, further including defining the portions of theenvironment in accordance with a type of collection device capturing thebrightness value sequences.
 19. The method as defined in claim 14,wherein the threshold amount corresponds to a size of a body part. 20.The method as defined in claim 14, wherein the brightness valuesequences are provided by an array of sensors incapable of identifyingdetected persons.
 21. The method as defined in claim 14, furtherincluding identifying a first location of a face in the environmentusing thermal imaging data.
 22. The method as defined in claim 21,further including comparing the first location of the face to a secondlocation associated with the first group of brightness value sequences.23. The method as defined in claim 21, further including adjusting apeople count when the first location of the face does not correspond toa second location associated with the first group of brightness valuesequences.
 24. The method as defined in claim 14, further includingidentifying a first location of a non-face body part in the environmentusing thermal imaging data.
 25. The method as defined in claim 24,further including comparing the first location of the face to a secondlocation associated with the first group of brightness value sequences.26. The method as defined in claim 24, further including discarding anindication of the presence of the first person when the first locationis similar to a second location associated with the first group ofbrightness value sequences.
 27. A tangible computer readable storagemedium comprising instructions that, when executed, cause a machine toat least: access brightness value sequences respectively correspondingto portions of an environment; detect a first one of the brightnessvalue sequences that exhibits a first waveform indicative of a firsthuman pulse and a second one of the brightness value sequences thatexhibits a second waveform indicative of a second human pulse, thesecond human pulse not synchronized with the first human pulse; identifya first group of the brightness value sequences proximate the firstportion and a second group of the brightness value sequences proximatethe second portion; determine whether a threshold amount of the firstidentified group of the brightness value sequences exhibit the firstwaveform and whether a threshold amount of the second identified groupof the brightness value sequences exhibit the second waveform; andindicate a presence of a first person in the environment when thethreshold amount of the first identified group of the brightness valuesequences exhibit the first waveform and a presence of a second personin the environment when the threshold amount of the second identifiedgroup of the brightness value sequences exhibit the second waveform. 28.The storage medium as defined in claim 27, wherein the instructions,when executed, cause the machine to associate the presence of the firstperson with media identifying information detected in the environment ata time corresponding to capture of the brightness value sequences. 29.The storage medium as defined in claim 27, wherein the instructions,when executed, cause the machine to determine whether the identifiedgroup of the brightness value sequences that exhibit the first waveformexceeds a second threshold.
 30. The storage medium as defined in claim29, wherein the instructions, when executed, cause the machine to, whenthe first identified group of the brightness value sequences thatexhibit the first waveform exceeds the second threshold, indicate apresence of more than one person.
 31. The storage medium as defined inclaim 27, wherein the threshold amount corresponds to a size of a bodypart.
 32. The storage medium as defined in claim 27, wherein thebrightness value sequences are provided by an array of sensors incapableof identifying detected persons.
 33. The storage medium as defined inclaim 27, wherein the instructions, when executed, cause the machine toidentify a first location of a face in the environment using thermalimaging data.
 34. The storage medium as defined in claim 33, wherein theinstructions, when executed, cause the machine to compare the firstlocation of the face to a second location associated with the firstgroup of brightness value sequences.
 35. The storage medium as definedin claim 33, wherein the instructions, when executed, cause a machine toadjust a people count when the first location of the face does notcorrespond to a second location associated with the first group ofbrightness value sequences.
 36. The storage medium as defined in claim27, wherein the instructions, when executed, cause the machine toidentify a first location of a non-face body part in the environmentusing thermal imaging data.
 37. The storage medium as defined in claim36, wherein the instructions, when executed, cause the machine tocompare the first location of the non-face body part to a secondlocation associated with the first group of brightness value sequences.38. The storage medium as defined in claim 36, wherein the instructions,when executed, cause the machine to discard an indication of thepresence of the first person when the first location is similar to asecond location associated with the first group of brightness valuesequences.
 39. The apparatus as defined in claim 1, wherein the lightinformation is provided by an array of light sensors horizontallyarranged to define the segments as vertical strips across theenvironment.
 40. The method as defined in claim 14, wherein thebrightness value sequences are provided by an array of sensorshorizontally arranged to define the segments as vertical strips acrossthe environment.
 41. The storage medium as defined in claim 27, whereinthe brightness value sequences are provided by an array of sensorshorizontally arranged to define the segments as vertical strips acrossthe environment.